658 results about "Feature mining" patented technology

The invention relates to the field of enterprise risk early warning analysis, in particular to an intelligent risk early warning method and system based on multi-dimensional data analysis. The method comprises the following steps: acquiring a multi-dimensional enterprise data stream, performing heterogeneous index information analysis and logic hierarchy reconstruction, and constructing an enterprise running state sensing map; performing multi-index local fluctuation amplitude calculation on the enterprise operation state sensing map, and performing dynamic disturbance feature mining to construct a risk disturbance spectrogram; performing deep semantic analysis and node state abrupt change feature analysis on the risk disturbance spectrogram to generate a dynamic transition type of an abrupt change node; and performing full-period time sequence tracing on the risk disturbance spectrogram, performing potential risk node prediction based on the dynamic transition type, and identifying other potential risk propagation ports. Through accurate and efficient enterprise risk perception, the risk intervention decision is made in advance, and the anti-risk capability and the operation stability of the enterprise are improved.

The present invention relates to the technical field of power system risk assessment, and provides a power system risk assessment method in extreme weather based on deep feature mining. The risk assessment method comprises: performing regional division on the whole power system on the basis of the geographic location; on the basis of the historical wind speed data, constructing a correlation model of the strong wind extreme weather in a plurality of regions of the power system; constructing a strong wind scenario sample set of each region on the basis of the correlation model; acquiring the power transmission line fault probability of the corresponding region under each strong wind scenario in the strong wind scenario sample set; randomly allocating a power system operation condition to each strong wind scenario of each region, and acquiring an operation risk value of the power system under the corresponding operation condition; constructing and training a risk assessment model; and using the risk assessment model to perform power system operation risk assessment. The present invention can implement real-time and accurate assessment of the power system operation risk, and can provide an auxiliary decision-making suggestion for the power system.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention discloses a GIS disconnecting switch multi-state intelligent sensing system, and relates to the field of GIS disconnecting switch state monitoring. A self-calibration multi-mode sensor is deployed for multi-source data acquisition, and novel sensors including terahertz imaging and the like are included; in data preprocessing, deep learning noise reduction is applied, fuzzy entropy is used for dynamic weighted fusion, and abnormal values are processed by an improved algorithm; feature extraction is combined with a plurality of frontier algorithms to process vibration signals, and CRNN is used to analyze acoustic signals; the QPSO evidence theory is adopted for state fusion perception, the node relation is learned by means of GNN, and the weight is adjusted according to the information gain rate; state assessment and early warning are based on transfer learning, GAN and LSTM-attention mechanisms, and early warning priorities are ranked by FAHP. According to the invention, multi-mode accurate acquisition, intelligent data processing, deep feature mining, innovative fusion perception, accurate evaluation and early warning and efficient fault diagnosis and positioning are realized, the state of the GIS isolation switch can be comprehensively and accurately perceived, the system is self-learned and optimized, the equipment safety is guaranteed, the risk of a power system is reduced, and the power operation and maintenance benefits are improved.

The invention relates to the technical field of deformation early warning of a coal mine TBM tunneling roadway, in particular to a simulation and monitoring method for intelligent early warning of large deformation of the coal mine TBM tunneling roadway. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, feature mining and fusion, intelligent early warning model construction and real-time monitoring and early warning, and the data acquisition and preprocessing specifically comprises the steps that multiple types of sensors are installed on a coal mine TBM tunneling roadway site to acquire multiple data, three-dimensional reconstruction is conducted on image data, and timestamps and space coordinates of the data are unified; a multi-dimensional spatio-temporal data set is formed by using wavelet transform denoising, and feature mining and fusion comprises the steps of extracting geologic structure image data features by using 3D-CNN and capturing spatio-temporal change features of other data by using LSTM. According to the method, high-quality data is provided, meanwhile, the model is more suitable for complex working conditions and more accurate, the generalization ability of the model is enhanced, real-time monitoring and early warning are timely, effective and visual, decision making is facilitated, and safety accidents and economic losses can be effectively reduced.

The invention relates to the technical field of carbon emission prediction, and discloses a combined carbon emission prediction method based on multi-source heterogeneous tensor data. The method comprises the steps that multi-source carbon emission data streams such as industrial emission, traffic flow and energy consumption in a target area are collected, and a carbon emission tensor sequence with the unified space-time dimension is generated through heterogeneous tensor conversion; multi-scale space-time correlation features in the sequence are extracted through a dynamic feature fusion algorithm, and a combined prediction model containing a long-period trend prediction branch and a short-period fluctuation prediction branch is constructed. And iteratively training the model by using a historical tensor sequence until convergence, and inputting a real-time multi-source data stream to output a combined prediction result. According to the method, effective integration and deep feature mining of multi-source heterogeneous data are realized, different change rules of carbon emission are accurately captured through branching model design, the comprehensiveness and reliability of prediction are improved, and scientific reference is provided for carbon emission management and control.

The invention relates to the technical field of computers, in particular to a task scheduling optimization analysis method and system based on big data. The method comprises the following steps: acquiring task related data, task system resource use data and task execution state data in real time in a corresponding distributed big data processing scene among a server, a storage device and a client; performing big data feature mining analysis and task scheduling clustering analysis by using a Hadoop distributed framework to generate task scheduling cluster groups corresponding to similar resource requirements; performing residual execution duration calculation and task emergency accounting on the task calculation nodes corresponding to the distributed big data processing scene to obtain task node scheduling emergency degrees; and performing scheduling priority determination and task scheduling optimization analysis on the corresponding task calculation nodes to generate a task dynamic optimization scheduling scheme. According to the method, the rationality of distributed task resource scheduling can be improved.

The invention provides a river crab feed feeding mixed monitoring method, system and device and a medium, and belongs to the field of aquaculture intelligent monitoring. Data is collected and normalized, and a chain type data collection system is constructed; the edge computing and block chain technology is adopted to realize data distributed storage and transmission; a fuzzy neural network hybrid model is constructed, fuzzification processing, feature mining and defuzzification output are integrated, and a feeding amount prediction value is generated; associating the feed proportioning library with the knowledge graph, and iteratively training the model and generating a multi-target feeding decision parameter; feeding equipment is controlled to execute actions according to the decision parameters, and the real-time monitoring model outputs and triggers an early warning signal; and updating the knowledge graph and the feed matching library based on the feedback data to form closed-loop self-adaptive regulation and control. According to the method, various factors can be comprehensively considered, scientific and intelligent feed feeding decision is realized, feed waste and insufficient feeding are effectively avoided, and the growth quality of the river crabs is improved.

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention relates to a big data-based passenger-roll transport demand prediction and ship intelligent scheduling method and system. The method comprises the steps of obtaining multi-source shipping data for a target area; inputting the multi-source shipping data into the spatial-temporal feature mining model, and predicting passenger rolling transportation demand information of the target area; acquiring ship real-time position, passenger carrying capacity, energy consumption data and port real-time operation state in the target area, and dynamically generating an optimal scheduling scheme by adopting a shipping scheduling model in combination with the predicted passenger transport demand information; the shipping scheduling model is obtained by interacting a decision scheduling model with an intelligent agent corresponding to the passenger roller transportation system and performing iterative training by adopting a reinforcement learning algorithm; and converting the optimal scheduling scheme into visual information, pushing the visual information to operation terminals of the ship and port workers so as to start corresponding shipping scheduling operation, and monitoring the execution effect of the shipping scheduling operation in real time.

The invention discloses a credential authenticity verification method based on credible block chain big data evaluation, and relates to the technical field of credential authenticity verification methods, and the method comprises the following steps: S1, collecting credential data from multiple sources, covering multiple formats and multiple channels, integrating specifications, and enabling the integrity rate to exceed 95%, S2, embedding data into a block chain, carrying out Hash salting encryption, and storing according to a consensus mechanism, s3, carrying out big data feature analysis, mining association, modeling by fusing internal and external features, and carrying out automatic updating optimization; and S4, comprehensively verifying voucher and priori basic information, and comparing the model with the history. According to the method provided by the invention, the verification accuracy is more than 95%, the authenticity of the voucher is accurately identified, block chain encryption ensures data security, tampering-free tracing is realized, big data analysis is combined with multi-source data and internal and external characteristics, comprehensive evaluation is realized, the automatic process is efficient, a user feedback optimization model is realized, the verification reliability is improved, and the use order of the voucher in each field is maintained.

In order to solve the problems that day lily plants are densely shielded and categories are difficult to distinguish, the invention provides a field dense day lily pixel classification and picking information acquisition method. The method comprises the steps of designing a double-branch decoder fusing local and global information, constructing a progressive stepped feature mining module (PLFM) and a multi-scale hierarchical category enhancement module (PCEN), optimizing hyper-parameters through a PALA algorithm, and constructing an adaptive double-branch loss enhancement and category enhancement network (DLCE-Net) to improve semantic segmentation performance. And designing a picking point positioning algorithm (MW-GCA) of Mini-Window guided angular point analysis based on a segmentation result, screening angular point coordinates through a U-shaped window, obtaining an attitude line segment, and realizing picking point pixel coordinate and angle estimation. The method can reduce the labor loss and accelerate the engineering landing of the automatic crop picking technology.

The invention belongs to the technical field of electric power system information processing, and particularly relates to an electric power information analysis method based on big data, through semantic fusion of multi-source heterogeneous data and dynamic feature mining of a time sequence attention mechanism, in a load prediction scene, compared with a traditional single data source model, the electric power information analysis efficiency is improved. After the meteorological data, the user power consumption behavior data and the power grid operation data are fused, the prediction average error rate is reduced; in an equipment fault early warning scene, through multi-dimensional correlation analysis of vibration signals, oil temperature data and environmental factors, transformer latent faults can be early warned in advance, and the fault identification accuracy is improved; meanwhile, a self-adaptive modeling engine and a closed-loop feedback mechanism enable the system to have a self-evolution capability: when the power grid topology is adjusted or the new energy grid-connected proportion is changed, the model does not need to be manually retrained, and self-adaptive adaptation can be completed in two scheduling cycles through dynamic feature weight adjustment and meta-learner parameter optimization, so that the analysis performance is maintained to be stable.

The invention discloses an ovarian tumor cross malignancy classification system based on a hybrid radiomics model. On one hand, a backbone network in the Med3D network is used as a depth feature extractor to construct a target network, and multi-modal 3D magnetic resonance image data is used to carry out fine tuning training on the target network to obtain a corresponding ovarian tumor classification network for obtaining depth features and tumor classification results. On the other hand, omics characteristics of the tumor region of interest are extracted, and a tumor classification result corresponding to the omics prediction model is obtained. In addition, the depth features and the omics features are fused, and a corresponding classification result is obtained through a KNN classification algorithm. And finally, obtaining a final ovarian tumor cross malignancy classification result according to weighted average of the three classification results. According to the invention, by integrating various models and algorithms, more comprehensive feature mining and analysis are carried out on the multi-modal MRI image data of the ovarian tumor patient, so that the final ovarian tumor classification effect is improved.

The invention discloses a marine environment real-time monitoring and early warning system based on machine learning, and relates to the technical field of machine learning. Comprising the steps that an ocean multi-source sensing module collects ocean environment data in real time through a sensor and a combined collection scheme; the multi-source feature extraction module performs time domain, change rate and frequency domain feature analysis on the data to construct a unified multi-dimensional feature vector; the multi-model fusion prediction module outputs a marine environment state vector through dynamic weighting and deviation correction based on a parallel learning architecture of a deep neural network, a long-short-term memory network and a one-dimensional convolutional neural network; and the ocean risk identification and early warning module generates graded and classified early warning information through double study and judgment of a sea condition classifier and an abnormal event detector. According to the method, comprehensive acquisition, deep feature mining, high-precision prediction and accurate early warning of marine environment data are realized, the problems of low prediction precision, risk identification lag and the like in the prior art are effectively solved, and reliable guarantee is provided for marine operation safety.

The invention provides a logistics transportation optimization method and system based on a traffic logistics large model, and the method comprises the steps: carrying out the element extraction through receiving a basic scheduling instruction of a logistics transportation task, generating core element information through semantic scene analysis, and carrying out the transportation scene adaption based on the core element information. Generating a scene influence factor set in combination with a depth feature mining result of the historical transportation scene database; then, calling a pre-trained traffic logistics large model to carry out collaborative path decision on the scene influence factor set, and outputting a candidate path scheme population containing a path topological structure and cost feature distribution through interactive iteration of a path generation unit and a cost evaluation unit in the model; and performing multi-round evolution screening on the candidate path scheme population according to a preset optimization target, determining an optimal path scheme meeting transportation requirements, and generating a transportation scheduling instruction set to be pushed to a transportation execution terminal after conversion. According to the invention, the adaptability, decision-making precision and reliability in the logistics transportation optimization process can be effectively improved.

The invention relates to the technical field of motor state monitoring and intelligent fault diagnosis, in particular to a motor test and diagnosis system and method based on multi-data acquisition, and the system comprises a multi-mode sensing array module, a heterogeneous data fusion module, a coupling feature mining module and a migration diagnosis decision module. Wherein the multi-mode sensing array module is used for collecting mechanical vibration time domain signals, temperature field space distribution data and near-field electromagnetic radiation spectrums of a motor; the heterogeneous data fusion module is used for generating a time-frequency matrix, a thermal field characteristic matrix and fundamental frequency harmonic energy distribution characteristics; the coupling feature mining module is used for generating a comprehensive diagnosis factor; and the migration diagnosis decision module is used for constructing a feature mapping network based on meta-learning and outputting a fault type and a confidence score. According to the invention, through multi-modal feature fusion and a metalearning-based migration diagnosis mechanism, high-precision identification and cross-model adaptive diagnosis of multi-source fault information under a complex working condition of the motor are realized.

The invention provides a bioelectrical impedance lung cancer detection method based on artificial intelligence, which comprises the following steps: acquiring multi-dimensional electric signal data of a tissue sample, preprocessing the data, and acquiring preliminary feature representation of the sample through a static spatial feature extraction method; based on the time sequence characteristics of the signals, constructing a dynamic feature extraction model, extracting dynamic features from time sequence association, and mining the difference and regularity between samples; static space features and dynamic time sequence features are fused, multi-dimensional attributes of signals are comprehensively analyzed, high-dimensional key feature representation is generated, the distinguishing capacity and reliability of the features are improved, and attention to the key features is enhanced; according to the method, the characteristics of a sample are fused, the fused characteristics are analyzed and classified, the normal state and the abnormal state of the sample are judged in combination with model output, finally, accurate detection and diagnosis results are achieved, the performance of the method on various bioelectrical impedance data sets is superior to that of a traditional method, and the sensitivity and specificity of lung cancer detection are effectively improved.

The invention relates to a speech emotion recognition method and system based on multi-scale adaptive feature fusion. A speech signal is acquired and preprocessed to obtain a Mel-frequency cepstrum coefficient; time features and frequency features of Mel-frequency cepstrum coefficients are extracted and fused to obtain multi-scale features, the multi-scale features are divided into a global information estimation branch and an efficient self-attention branch through channel expansion, the efficient self-attention branch extracts fine-grained local features through self-attention, the global information estimation branch extracts low-frequency content through downsampling, and the high-efficiency self-attention branch extracts low-frequency content through down-sampling. Non-local information is captured in combination with global variance modulation; by fusing the global features and the local features obtained by the two branches, the relevance between different features is mined, deep feature representation is obtained, and fused deep time-frequency features are obtained; and classifying the fused deep-layer time-frequency features by using a full-connection network, and determining an emotion category corresponding to the voice signal.

The invention relates to the technical field of data analysis, and provides a wind turbine generator health assessment method and system based on AI multi-source data fusion, which are used for realizing accurate and dynamic wind turbine generator health assessment, and the method comprises the following steps: collecting a first operation data set of a wind turbine generator through a set sensor network, carrying out multi-dimensional state feature mining to obtain a multi-dimensional state feature set; combining the feature set, utilizing a pre-constructed state deduction model to carry out state deduction iteration, introducing different initial conditions and parameter perturbation to carry out multi-path deduction, and generating a plurality of state deduction iteration results; carrying out fusion processing based on conflict resolution on a state deduction iteration result, and obtaining a state deduction fusion result in combination with the feature relevance weight matrix and a credibility evaluation mechanism; and finally, performing state evaluation based on synchronous time sequence analysis on a state deduction fusion result and a second operation data set of which the acquisition moment is later than that of the first operation data set to obtain a health state evaluation label of the wind turbine generator.

The invention discloses a wind and light output scene generation method based on depth feature mining and adaptive clustering, and the method comprises the steps: cleaning wind and light output data, carrying out the normalization processing of the data, and enabling the data to be mapped to a preset interval, so as to eliminate the dimension influence; constructing a deep convolutional feature extraction network to extract a corresponding feature map from the normalized wind and light output data; a K-Means + + algorithm is adopted to initialize a clustering center, an improved ISODATA clustering algorithm is executed based on a density threshold dynamic splitting mechanism, clustering parameters are optimized through Bayesian optimization, and a typical scene is generated. The accuracy of the wind and light output scene is remarkably improved.

The invention provides a method for establishing an ecological resource intelligent credible data element model. The method comprises the following steps: acquiring natural ecological basic data; obtaining natural ecology derivative data, wherein the natural ecology derivative data comprises ecological tourism description metadata and leisure health care description metadata; performing controlled feature mining on the natural ecology basic data and the natural ecology derivative data to generate ecological basic data elements and ecological derivative data elements; performing key feature extraction on the ecological basic data elements and the ecological derivative data elements to obtain basic ecological key features and derivative ecological key features; and obtaining green financial data elements, and establishing association relationships between the green financial data elements and the basic ecological key features and between the green financial data elements and the derivative ecological key features to generate an ecological resource credible data element model. According to the technical scheme, the problem that effective association between green financial data elements and ecological resource data is lacked is at least solved.

The invention relates to a copper-clad plate production line collaborative regulation and control method based on digital twinning, and the method comprises the steps: constructing a multi-level data set of dynamic virtual-real mapping through full-process digital twinning modeling, process data normalization and multi-level feature tagging, achieving the high consistency of a physical production line working condition and a simulation system, and achieving the collaborative regulation and control of a copper-clad plate production line in combination with an autonomous scheduling intelligent agent. Real-time perception and joint feature mining of multi-dimensional data such as equipment load, health degree and energy consumption are realized, the real-time adaptive scheduling capability of a production line to states such as sudden load and equipment aging is improved, scheduling target weights are periodically and adaptively generated, optimal instant balance of multi-target income is realized, and the optimal real-time scheduling capability of the production line is realized by adopting an evolutionary game and swarm intelligent optimization. A cross-device optimal resource scheduling scheme under the multi-target constraint is obtained, an evolution model is continuously fed back through real-time production data, a scheduling-execution-calibration-rescheduling closed-loop mechanism is formed, and the self-learning and long-period stability capabilities of a scheduling system are remarkably improved.

The invention discloses a transformer fault detection method and device based on an automatic encoder and multi-scale feature fusion, and belongs to the technical field of power transformer fault detection.The method comprises the steps that sound signals during operation of a transformer are collected, and data frame division and normalization preprocessing are conducted on the collected sound signals; reconstructing the data of the collected sound signals by using an automatic encoder trained by normal sound data, and extracting the data coded by the automatic encoder as the implicit features of the sound signals; according to the error before and after reconstruction, sound abnormity judgment is carried out, and fault type detection is further carried out on abnormal data by using a classifier; and the classifier receives the collected sound data and the characteristic signal extracted by the automatic encoder as input, and classifies fault types. According to the method, the classifier is arranged, fault features are mined through the multi-scale features of the transformer sound signals, and the accuracy of transformer fault classification is improved.

The invention provides a wind turbine generator transmission chain fault detection method and device, and relates to the technical field of fault detection.The wind turbine generator transmission chain fault detection method comprises the steps that time sequence operation data and state waveform data of a wind turbine generator transmission chain are obtained; determining an abnormal characteristic index of the transmission chain of the wind turbine generator according to whether the time sequence operation data meets a preset target rule or not; according to the state waveform data, determining a time domain characteristic index and a frequency domain characteristic index of a transmission chain of the wind turbine generator; and based on the abnormal feature index, the time domain feature index and the frequency domain feature index, determining fusion features, and determining a fault detection result of the wind turbine generator transmission chain according to the fusion features, thereby solving the technical problems of incomplete data feature mining and low fault detection accuracy in the prior art.

The invention relates to the technical field of Internet of Things safety management, in particular to a special equipment Internet of Things informatization management system which comprises an assembly reading module, an inspection evolution module, a difference recognition module, a manufacturing hooking module and a credible screening module. According to the method, the initial value of the equipment identification chain is generated through the dynamic splicing assembly data and the installation record, the traceability of the full-life-cycle data of the equipment is enhanced, the dynamic characteristics of the multi-period connection parameters and the function state are extracted by adopting the long-short-term memory network, the running state jump of the equipment is identified, and the real-time performance and continuity of anomaly detection are improved; constructing a data comparison mechanism, analyzing the difference between a manufacturing timestamp and a functional state, forming equipment degradation path visualization, enhancing maintenance decision data support, fusing multi-dimensional data, breaking through the limitation of single-dimensional analysis, and improving the safety management predictive maintenance capability of special equipment through time sequence feature mining and data collaborative verification; and the systematic risk caused by state mutation is reduced.

The invention discloses an intelligent drought risk management method and system based on disaster attribution analysis. The method comprises the steps of data acquisition, data enhancement, disaster attribution analysis, disaster dynamic risk assessment and intelligent drought risk management. The invention relates to the field of drought risk management, and adopts an intelligent drought risk management integrated method combining disaster attribution analysis and disaster dynamic risk assessment to perform drought disaster dynamic analysis from the angle of attribution, and perform multi-factor feature mining in combination with a plurality of feature optimization models. The complexity processing capability and the real-time precision of intelligent drought risk management are improved, and the fusion performance of multi-field data is also improved; performing disaster attribution analysis by adopting an improved support vector machine model combining an expert model and graph neural network feature optimization; and carrying out disaster dynamic risk assessment by adopting a gating circulation unit which combines the generative adversarial network and the graph convolution network and is deepened in feature.

The invention discloses a multi-user short-term power load prediction method for an improved Transform model, and relates to the technical field of novel power systems, and the method comprises the steps: S1, setting an internal decomposition block based on a moving average and Fourier transform thought, integrating the internal decomposition block into a frame, and decomposing an input data sequence into trend, season, holiday and festival variables and residual variables; s2, an Nystrom method is used for approximating a softmax matrix in a standard self-attention mechanism, and the softmax matrix is used for coping with challenges of secondary time complexity and memory use in Transform; s3, performing deep feature mining on the seasonal part after time sequence decomposition by using convolution operation; and S4, probability prediction based on Monte Carlo random inactivation. And designing an internal decomposition module of the depth model, and extracting an internal complex time sequence trend of a hidden state in the model by the module, so that the model has a progressive decomposition capability of a complex time sequence. Different from a traditional load prediction method for exploring time characteristics of a single user, a space-time attention mechanism is improved, and accurate multi-user short-term load prediction is provided.

The invention discloses an intelligent pest and disease damage monitoring system based on multi-modal data fusion of an unmanned aerial vehicle, and the system carries a multi-source heterogeneous data collection module through the unmanned aerial vehicle, obtains optical, multispectral, thermal infrared and other multi-modal data in parallel, and stores the data in a ground end after wireless link coding transmission and format adaptation. According to the system, an improved YOLO algorithm is used to construct a multi-level feature pyramid and an adaptive convolution kernel architecture, and feature extraction and disease and pest candidate region generation are carried out on multi-modal data; time sequence dynamic analysis and depth feature mining are realized by means of an optimized long-short-term memory network in combination with parameters such as a pest and disease damage growth cycle; and through a weight adaptive adjustment fusion strategy, decision-level fusion is carried out on results of the two algorithms, and final monitoring data is output. Besides, various correlation formulas are designed in each link of the system, so that parameters in multiple aspects of data acquisition, algorithm processing and the like are mutually coordinated, efficient and accurate monitoring of diseases and insect pests is realized, and technical support is provided for prevention and control of agricultural diseases and insect pests.

The invention relates to the field of data fusion evaluation, and provides a dynamic quantitative evaluation method for an enterprise development process, which comprises the following steps of: S1, acquiring operation data and compliance data of an enterprise through a multi-source heterogeneous data interface, and executing entity association and conflict resolution operation to generate a structured basic data set; s2, extracting dynamic evaluation features of the structured basic data set through a data analysis engine, wherein the data analysis engine comprises an industry adaptive rule base and / or a deep feature mining network; s3, calculating a multi-dimensional score value of the enterprise development situation based on the dynamic evaluation features; and S4, according to a historical evaluation record, mapping the multi-dimensional score value to a preset grade interval and presenting the multi-dimensional score value. And furthermore, objective and real-time evaluation is carried out on the growth performance and the anti-risk capability of the non-listed companies.